Evidence map›Paper›PMID 38452455›Full record

ArticleEpidemics2024

Projecting the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak.

Sean Moore, Sean Cavany, T Alex Perkins, Guido Felipe Camargo España

Abstract read
In one paragraph

Article in Epidemics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sean MooreDepartment of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States. Electronic address: smoore15@nd.edu.
Sean CavanyDepartment of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States.
T Alex PerkinsDepartment of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States.
Guido Felipe Camargo EspañaDepartment of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, United States.

Funding

Model-based inference and forecasting of co-circulating pathogen dynamicsR35GM143029 · NIGMS · UNIVERSITY OF NOTRE DAME · PI PERKINS, ALEX · 2021 to 2025
$2.0M
Assessing the impact of COVID-19 interventions on human mobility and SARS-CoV-2 transmission dynamics in the United StatesR21AI164391 · NIAID · UNIVERSITY OF NOTRE DAME · PI MOORE, SEAN MICHAEL · 2021 to 2022
$430k
NIAID NIH HHS R21 AI164391NIGMS NIH HHS R35 GM143029
6 · The paper itself

Abstract

Over the past several years, the emergence of novel SARS-CoV-2 variants has led to multiple waves of increased COVID-19 incidence. When the Omicron variant emerged, there was considerable concern about its potential impact in the winter of 2021-2022 due to its increased fitness. However, there was also considerable uncertainty regarding its likely impact due to questions about its relative transmissibility, severity, and degree of immune escape. We sought to evaluate the ability of an agent-based model to forecast incidence in the context of this emerging pathogen variant. To project COVID-19 cases and deaths in Indiana, we calibrated our model to COVID-19 hospitalizations, deaths, and test-positivity rates through November 2021, and then projected COVID-19 incidence through April 2022 under four different scenarios that covered the plausible ranges of Omicron's severity, transmissibility, and degree of immune escape. Our initial projections from December 2021 through March 2022 indicated that under a pessimistic scenario with high disease severity, the peak in weekly COVID-19 deaths in Indiana would be larger than the previous peak in December 2020. However, retrospective analyses indicate that Omicron's severity was closer to the optimistic scenario, and even though cases and hospitalizations reached a new peak, fewer deaths occurred than during the previous peak. According to our results, Omicron's rapid spread was consistent with a combination of higher transmissibility and immune escape relative to earlier variants. Our updated projections starting in January 2022 accurately predicted that cases would peak in mid-January and decline rapidly over the next several months. The performance of our projections shows that following the emergence of a new pathogen variant, models can help quantify the potential range of outbreak magnitudes and trajectories. Agent-based models are particularly useful in these scenarios because they can efficiently track individual vaccination and infection histories with multiple variants with varying degrees of cross-protection.

Indexed as

COVID-19SARS-CoV-2Disease OutbreaksForecastingHumansIncidenceIndianaUncertaintyCOVID-19ForecastingOmicronSARS-CoV-2Variant

Identifiers

PMID38452455
PMCPMC11493339

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.